As
Psychology students, many of us know by now that statistics is a fundamental
part of psychological research. We have all had our moments grappling with the
intricacies of ANOVA and the multiple functions of SPSS, commonly, the first data
analysis software introduced to us in our foundational psychology statistical
module. However, the main drawback of SPSS is that it is not free.
Thankfully,
there are many readily available open-source data analysis programmes. For
today, we will be introducing the basics of R, one of the more well-known
open-source programmes.
What is R?
R is free software environment for
statistical computing and programming. It is the brainchild of Ross Ihaka and
Robert Gentleman, conceived at the Department of Statistics in the University
of Auckland.
What can R bring to
the table that SPSS doesn’t already offer?
- R is completely
free of charge.
- R is constantly being updated by members of
the community with new features and bug-fixes.
- R
is incredibly versatile, with functions that allow simple tabulation of summary
statistics to the creation of extensive graphical diagrams.
- R is a programming language. When you learn how to use R, besides gaining valuable data analysis skills, you are picking up programming skills as well.
Programming?!
For those
who have not tried their hand at programming, this may seem incredibly daunting
but do not fear! There are plenty of existing R scripts readily available
online for you to use for your data analysis.
Also, RStudio,
another completely free interface,
makes programming in R much more manageable, such as making helpful suggestions/corrections
of functions.
If you are ever
unsure of methods of analysis using R, there are plenty of free online resource
guides to refer to. Here are some examples:
And even if
you can’t find exactly what you are looking for within the guides, generally,
suggestions can be found with a simple Google search.
Enough said, how do I
install R/RStudio on my computer/laptop?
Alternatively,
RStudio can also be accessed through your web browser for online use here:
(Note:
Certain functions on RStudio Cloud may differ from the ones I will be
mentioning below.)
Great, I installed
everything.
So where do I begin?
So where do I begin?
Let me
begin by introducing the four RStudio windows to you. (Note: This view might
look slightly different on a non-Apple laptop/computer but the basic elements
are the same.)
1. Source
The “Source”
panel is where you create R Scripts, another name for codes. You can type in
your code here. But remember to click the “Run” button to evaluate your code.
Let’s start
with something simple: 5+5
Then we click
on “Run”.
The output,
“10” appears in the “Console”.
Note: When
you close the RStudio window and reopen it, your “Source” panel will be
cleared. In order to prevent this, you can save the untitled file under a file
name before working on your code.
2. Console
In the “Console”
panel, the codes that are typed in can be immediately evaluated when you press
the enter key. However, the code in the “Console” is cleared whenever you close
the window, regardless of whether you saved it as a file in the beginning. It
is advisable to write most of your code in the source pane instead of the
console pane to avoid losing your progress.
3. Environment/ History
The “Environment”
tab displays the data objects that you defined in your R session, such as those
you have in your data frame. For instance, in a previous R session, my
“Environment” tab displayed the different variables that I defined and their
values:
The
“History” tab keeps track of the commands that you recently typed. To help with
visualization, it will look something like this:
4. Files/ Plots/ Packages/ Help
The “Files”
tab shows you a directory of the files that you have on your hard drive. One
useful function of this tab is that it allows you to set a folder that you
would like to save files to. To do this, navigate to the folder and click on
“More” and select “Set As Working Directory” from the dropdown.
The “Plots”
tab shows you the plots that you have created. You can choose to export the
plot in .pdf, .jpeg format or simply copy the plot and paste it where you
desire.
The
“Package” tab shows you the R packages which are currently installed on your
hard drive. R packages are collections of functions that are ready for use once
downloaded. For instance, installing the “psych” R package allows you to
calculate kurtosis. To ensure your desired package is loaded for use in your R
session, make sure that the box next to it is has been checked.
The “Help”
tab allows you to look up different R functions. For instance, if I want to
learn more about the “psych” R package, I can type “psych” into the search bar.
How do I import my
existing dataset into R?
Go to the
“Environment” tab and click on “Import Dataset”. From the dropdown, you can
select which file format to import. R supports a variety of data files, from
Excel files to CSV files.
When you
select an option, this screen will pop up:
You can use
“Browse” to search for the file with the dataset you desire. You can also name
the dataset for easy reference in R.
Any final tips for now?
- It would be great for you to note that R is case-sensitive, meaning that you should be particular about the usage of uppercase and lowercase letters when typing your code.
- Another useful tool is the commenting function in R. You can type “#” into the “Console” or “Source” panel and everything in the same line after the “#” will be ignored by R. This allows you to take notes and keep track of your thoughts while doing data analysis.
- If ever in doubt, rely on Google. The wonderful thing about open-source programmes is that since they are so easily accessed, there will probably be people in the community who have put out information on the functions that you require.
Have fun
exploring the new world of data analysis through R and stay curious!
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